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Probabilistic optimization of the dose coverage – applied to radiotherapy treatment planning of cervical cancer
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Radiation Science. (Anders Ahnesjö)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Radiation Science. (Anders Ahnesjö)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medical Radiation Science. (Anders Ahnesjö)
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Probabilistic (or robust) optimization is an alternative to margins for handling geometrical uncertainties in treatment planning of radiotherapy where the uncertainties are explicitly incorporated in the optimization through sampling of treatment scenarios. We present a probabilistic method based on statistical measures close to those behind conventional margin based planning. The dose planner requests a dose coverage to a specified probability, which the algorithm then attempts to fulfil.

We define the Percentile UnderDosage (PUD) as a measure of the target minimum dose coverage probability, i.e. the dose coverage that a treatment plan meet or exceed to a given probability. Margin based planning commonly use the implicit probabilistic treatment criteria that the 90th PUD is at least 95% of the intended dose. For optimization we use the Expected Percentile UnderDosage (EPUD) defined as the average dose coverage below a given PUD. The EPUD is, in contrast to PUD, a convex measure and hence standard optimization techniques can be used to find the optimal treatment plan. We propose an iterative method where a treatment optimization is performed at each iteration and the EPUD tolerance is adjusted gradually until a desired PUD is met.

We demonstrate our proposed probabilistic planning method for cervical cancer patients. The uncertainty caused by organ deformation is explicitly included in the probabilistic optimization where a statistical shape model is used to sample scenarios with different deformations. For all patients in this work, the iterative process of finding the EPUD tolerance converged in less than 10 iterations to within 0.1Gy of the requested PUD even though a conservative update scheme was used. The resulting estimated PUD was validated based on 1000 simulated scenarios not part of the optimization yielding an agreement within 1.2% of the requested PUD.

National Category
Other Physics Topics
Research subject
Medical Radiophysics
Identifiers
URN: urn:nbn:se:uu:diva-304982OAI: oai:DiVA.org:uu-304982DiVA: diva2:1034218
Available from: 2016-10-11 Created: 2016-10-11 Last updated: 2016-10-11
In thesis
1. Probabilistic treatment planning based on dose coverage: How to quantify and minimize the effects of geometric uncertainties in radiotherapy
Open this publication in new window or tab >>Probabilistic treatment planning based on dose coverage: How to quantify and minimize the effects of geometric uncertainties in radiotherapy
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Traditionally, uncertainties are handled by expanding the irradiated volume to ensure target dose coverage to a certain probability. The uncertainties arise from e.g. the uncertainty in positioning of the patient at every fraction, organ motion and in defining the region of interests on the acquired images. The applied margins are inherently population based and do not exploit the geometry of the individual patient. Probabilistic planning on the other hand incorporates the uncertainties directly into the treatment optimization and therefore has more degrees of freedom to tailor the dose distribution to the individual patient. The aim of this thesis is to create a framework for probabilistic evaluation and optimization based on the concept of dose coverage probabilities. Several computational challenges for this purpose are addressed in this thesis.

The accuracy of the fraction by fraction accumulated dose depends directly on the accuracy of the deformable image registration (DIR). Using the simulation framework, we could quantify the requirements on the DIR to 2 mm or less for a 3% uncertainty in the target dose coverage.

Probabilistic planning is computationally intensive since many hundred treatments must be simulated for sufficient statistical accuracy in the calculated treatment outcome. A fast dose calculation algorithm was developed based on the perturbation of a pre-calculated dose distribution with the local ratio of the simulated treatment’s fluence and the fluence of the pre-calculated dose. A speedup factor of ~1000 compared to full dose calculation was achieved with near identical dose coverage probabilities for a prostate treatment.

For some body sites, such as the cervix dataset in this work, organ motion must be included for realistic treatment simulation. A statistical shape model (SSM) based on principal component analysis (PCA) provided the samples of deformation. Seven eigenmodes from the PCA was sufficient to model the dosimetric impact of the interfraction deformation.

A probabilistic optimization method was developed using constructs from risk management of stock portfolios that enabled the dose planner to request a target dose coverage probability. Probabilistic optimization was for the first time applied to dataset from cervical cancer patients where the SSM provided samples of deformation. The average dose coverage probability of all patients in the dataset was within 1% of the requested.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2016. 51 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1264
Keyword
Radiotherapy, treatment simulation, probabilistic planning, dose calculation, probabilistic optimization, statistical shape model
National Category
Other Physics Topics
Research subject
Medical Radiophysics
Identifiers
urn:nbn:se:uu:diva-304180 (URN)978-91-554-9720-0 (ISBN)
Public defence
2016-11-25, Skoogsalen, Ing. 78-79, Akademiska Sjukhuset, Uppsala, 13:00 (English)
Opponent
Supervisors
Available from: 2016-11-03 Created: 2016-10-03 Last updated: 2016-11-16

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